AI-Enabled Smart Irrigation for Climate-Resilient Agriculture - Khan Roohee; Sharma Pooja (SHS Web of Conferences)
For agricultural productivity, climate change is a huge challenge, especially in water scarce and extremity of weather sensitive regions. Luckily, traditional irrigation methods have limitations in terms of addressing these challenges in the manner they require. Among others, this research proposes and develops an AI enabled smart irrigation system meant to improve climate resilience of agriculture. The system tries to achieve reduction in waste, optimized water usages and enhancement of crop yield by assimilating advanced machine learning algorithms with real time sensor data. Thus, proposing the solution of using predictive analysis for the prediction of the weather pattern, soil moisture level and crop water need, which bases its adaptive irrigation strategies upon the changing climatic conditions. The system includes the implementation of decision support tools for farmers to make decisions in by the line of sustainable agricultural practices. Field trials will evaluate the effectiveness of the system, and it will determine if the system increases water conservation, supports crop health and increases agricultural productivity. In this paper, we describe a novel, yet highly promising, approach aimed towards solving the most paramount requirement for adapting to climate uncertainty through agricultural technologies, necessary for achieving climate resilience and sustainable development.